Umbilical cord blood (UCB) is a rich source of hematopoietic cells that can be used to replace bone marrow components. Many blood disorders and systemic illnesses are increasingly being treated with stem cells as regenerative medical therapy. Presently, collected blood has been stored in either public or private banks for allogenic or autologous transplantation. Using a specific keyword, we used the English language to search for relevant articles in SCOPUS and PubMed databases over time frame. According to our review, Asian countries are increasingly using UCB preservation for future use as regenerative medicine, and existing studies indicate that this trend will continue. This recent literature review explains the methodology of UCB collection, banking, and cryopreservation for future clinical use. Between 2010 and 2022, 10,054 UCB stem cell samples were effectively cryopreserved. Furthermore, we have discussed using Mesenchymal Stem Cells (MSCs) as transplant medicine, and its clinical applications. It is essential for healthcare personnel, particularly those working in labor rooms, to comprehend the protocols for collecting, transporting, and storing UCB. This review aims to provide a glimpse of the details about the UCB collection and banking processes, its benefits, and the use of UCB-derived stem cells in clinical practice, as well as the ethical concerns associated with UCB, all of which are important for healthcare professionals, particularly those working in maternity wards; namely, the obstetrician, neonatologist, and anyone involved in perinatal care. This article also highlights the practical and ethical concerns associated with private UCB banks, and the existence of public banks. UCB may continue to grow to assist healthcare teams worldwide in treating various metabolic, hematological, and immunodeficiency disorders.

Underwater wireless sensor network (UWSN) has limited bandwidth, long propagation delays, and unrechargeable batteries, which forces the development of techniques minimizing the energy consumption of UWSNs. One solution is clustering that outperforms in terms of energy optimization and, in addition, can also provide scalability and data reliability. The development of clustering algorithms depends on UWSN architectures. In our case, we have proposed a hybrid UWSN network architecture named ACOP-UWSN (ACoustic OPtical Underwater Wireless Sensor Network), which aims for reliable data transmission at the fastest speed with higher data rates and minimized propagation delays. In ACOP-UWSN, optical sensors sense data, whereas acoustic sensors only relay data towards the surface buoy. Clustering of all these nodes may result in unnecessary wastage of the node’s energy and memory. Therefore, we undergo clustering of only optical sensors, as comparatively, sensing and processing of data consume maximum energy and memory than relaying data. In the proposed clustering algorithm, the criteria for cluster head selection are OSNR (optical signal-to-noise ratio) and residual energy. The mathematical derivation of OSNR for underwater wireless optical communication is a key part of the algorithm. The results achieved using the simulation tool show that our proposed OSNR-based energy efficient clustering algorithm (OECA) is efficient in terms of energy and as well data reliability as compared to state of art. The quantitative analysis of the results showed that the total energy consumed for the existing scheme is 450 J, which is more as compared to 400 J of OECA. Similarly, the residual energy of nodes using OECA is 1% greater than the existing scheme. To our knowledge, research on the clustering of optical sensors in UWSN is least available, and we are among the few to work on an OSNR-based clustering algorithm for underwater optical nodes.

This study introduces a novel meta-learning method to enhance diabetes detection using wearable sensor systems in smart health applications. Wearable sensor technology often needs to operate accurately across a wide range of users, each characterized by unique physiological and behavioral patterns. However, the specific data for a particular application or user group might be scarce. Moreover, collecting extensive training data from wearable sensor experiments is challenging, time-consuming, and expensive. In these cases, meta-learning can be particularly useful. This model can quickly adapt to the nuances of new users or specific applications with minimal data. Therefore, to solve the need for a huge amount of training data and to enable the application of artificial intelligence (AI) in data-scarce scenarios, a meta-learning method is proposed. This meta-learning model has been implemented to forecast diabetes, resolve cross-talk issues, and accurately detect R peaks from overlapping electrocardiogram (ECG) signals affected by movement artifacts, poor electrode contact, electrical interference, or muscle activity. Motion artifacts from body movements, external conditions such as temperature, humidity, and electromagnetic interference, and the inherent quality and calibration of the sensor can all contribute to noise. Contact quality between the sensor and the skin, signal processing errors, power supply variations, user-generated interference from activities like talking or exercising, and the materials used in the wearable device also play significant roles in the overall noise in wearable sensor data and can significantly distort the true signal, leading to erroneous interpretations and potential diagnostic errors. Furthermore, discrete wavelet transform (DWT) was also implemented to improve the quality of the data and enhance the performance of the proposed model. The demonstrated results confirmed that with only a limited amount of target data, the proposed meta-learning and DWT denoising method can adapt more quickly and improve the detection of diabetes compared to the traditional method. Therefore, the proposed system is cost-effective, flexible, faster, and adaptable, reduces the need for training data, and can enhance the accuracy of chronic disease detection such as diabetes for smart health systems.

In this article, we propose a novel method that integrates deep learning with Fabry–Pérot liquid crystal (FP–LC) technology for fiber Bragg grating (FBG) interrogation. The use of FP–LC enhances the measurement range and enables high sensitivity in FBG sensors, making them appropriate for a wide range of applications requiring precise and responsive sensing. However, collecting a large amount of real experimental FBG sensor data is time-consuming, technically challenging, and resource-intensive. To address this issue, we utilize a conditional generative adversarial network (CGAN) to generate a sufficient amount of synthetic training data. The CGAN generates data conditioned on real FBG sensor data, ensuring that the generated data closely looks like real experimental data distributions, which is crucial for effective model training. Moreover, we proposed a convolutional Neural Network (CNN) method to solve crosstalk problems, to improve sensing accuracy, and to precisely detect the peak wavelength of each FBG sensor. The experimental results demonstrated that the proposed CGAN technique effectively generates a large amount of data to improve the performance of the proposed CNN model. Furthermore, the results proved that the CNN trained on CGAN-generated data significantly improves the detection speed and accuracy of central wavelength measurements compared to traditional approaches. Hence, the proposed system is cost-effective, easy to set up for experiments, increases the feasibility and portability of modularization, fast and flexible, overcoming data shortages and improving the sensing accuracy of wavelength detection for FBG sensor systems.

Making the best use of expensive distributed temperature sensing (DTS) systems and saving costs is helpful for their measurement applications. The integration of free space optics (FSO) and DTS systems helps to reduce the cost of laying fiber optic cables and maintaining optical cables. In addition, machine learning is a common means to optimize the measurement demodulation of distributed fiber sensing. This work uses meta-learning to demodulate the measurement results of DTS. Meta-learning allows the use of small amounts of data to work, thereby reducing the data modeling cost and time of actual measurement applications. The atmospheric turbulence caused by the introduction of FSO or the attenuation or scattering caused by long-distance optical fibers may introduce noise into the temperature distribution curve measured by DTS. Therefore, this work also simulates the temperature distribution curve with noise and applies the discrete wavelet transform (DWT) to remove noise to reduce the data quality degradation caused by noise. Results show that the proposed meta-learning model can adapt faster and significantly improve the temperature event detection performance. Therefore, the proposed FSO and meta-learning system are cost-effective for applications in DTS and can enhance the distributed fiber optic sensing system.

The economies of all the countries are growing with the passage of time. In order to promote the businesses in the global markets, technology managers/technical brokers play a key role such as in the optimal allocation of scientific and technological resources. The industrialization of scientific and technological achievements is also possible due to optimal utilization of resources and a balance between exports and imports. The technology managers should be competent enough to bridge the gaps between the resource requirements and the availability of the resources required to run the businesses smoothly. This paper is using multi-criteria methods for analyzing the qualities in the technical managers which can assist the managers to run the businesses in an efficient manner to compete in the global markets. The interviews and questionnaire are used to collect the primary data. The opinion of experts is also considered. Then, SPSS 22.0 statistical analysis software is used to analyze the reliability and validity of the factors considered in the questionnaire. Then by using statistical techniques, a quantifiable competency model of technology managers is constructed. The multi-criteria AHP (analytical hierarchical process) method is used to compare the competency characteristic indexes of each level in pairs and quantitatively describe their importance of the criteria. It also assigns weights to judge the importance of each competency characteristic index. Since the evaluation of technical managers’ competency will be affected by various factors as mentioned in the paper, the fuzzy comprehensive evaluation method is also used to transform the evaluation dimension to accurately evaluate the competency of the technical manager which assists to judge the effectiveness of model evaluation. The proposed method provides theoretical and practical basis for the selection, evaluation, and cultivation of technical managers. It provides accuracy in results by 94% by adopting the statistical techniques.